52 citations · 166 across the 28 of their papers we have counts for
5 papers · 1 filter
Differentially Private Synthetic Control
Saeyoung Rho, Rachel Cummings, Vishal Misra
Synthetic control is a causal inference tool used to estimate the treatment effects of an intervention by creating synthetic counterfactual data. This approach combines measurement…
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
Chris Waites, Rachel Cummings
Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density…
Optimal Local Explainer Aggregation for Interpretable Prediction
Qiaomei Li, Rachel Cummings, Yonatan Mintz
A key challenge for decision makers when incorporating black box machine learned models into practice is being able to understand the predictions provided by these models. One prop…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob +2
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversari…